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A CRM Analytics consultant at Cloud Kicks is trying to upload data using an External Data API and the CSV file with the data was uploaded successfully. Upon analyzing the data using a lens, they find
they are unable to perform any mathematical operations as all the data and fields are treated as dimensions.
What is causing the problem?
Universal Containers (UC) uses a Microsoft Azure SQL Data Warehouse to gather information about sales reps' objectives. UC wants to use CRM Analytics to gain insights from this data and automatically load it into a CRM Analytics dataset daily. The data also needs to be transformed and merged with data from the company's org.
Which CRM Analytics user interface features should be used to complete these requirements?
A CRM Analytics consultant is adding direct data into an existing recipe. When trying to click on the input node, data preview is taking a long time to show any results.
How should the consultant resolve this?
What is a benefit of introducing a second local connector?
Introducing a second local connector in CRM Analytics can improve performance by enabling more granular control over data syncs. By having a separate connector, different datasets or recipes can be synchronized independently based on specific refresh needs, reducing load and improving overall performance. This approach helps optimize data flow operations, especially in large-scale deployments with varying data refresh requirements.
A consultant creates a CRM Analytics dashboard in a sandbox and it needs to be migrated into production.
What should the consultant use to complete the migration?
